# NiaAutoARM: Automated generation and evaluation of Association Rule Mining pipelines

arXiv: https://arxiv.org/abs/2501.00138 · HTML: https://mlsanity.com/paper/2501.00138/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 25 references that are in the map.
- First posted on arXiv 30 Dec 2024.

## Builds on (most cited first, 2 of 2)

- [AutoML: A Survey of the State-of-the-Art](https://mlsanity.com/paper/1908.00709/) (2019)
- [Automated Machine Learning -- a brief review at the end of the early years](https://mlsanity.com/paper/2008.08516/) (2020)

## Abstract (arXiv)

The Numerical Association Rule Mining paradigm that includes concurrent dealing with numerical and categorical attributes is beneficial for discovering associations from datasets consisting of both features. The process is not considered as easy since it incorporates several processing steps running sequentially that form an entire pipeline, e.g., preprocessing, algorithm selection, hyper-parameter optimization, and the definition of metrics evaluating the quality of the association rule. In this paper, we proposed a novel Automated Machine Learning method, NiaAutoARM, for constructing the full association rule mining pipelines based on stochastic population-based meta-heuristics automatically. Along with the theoretical representation of the proposed method, we also present a comprehensive experimental evaluation of the proposed method.